Format

Send to

Choose Destination
Cancer Immunol Res. 2020 Jan;8(1):108-119. doi: 10.1158/2326-6066.CIR-19-0476. Epub 2019 Nov 12.

Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer.

Author information

1
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
2
Department of Radiology-Cardiothoracic Imaging, University Hospitals, Cleveland, Ohio.
3
Department of Solid Tumor Oncology, Cleveland Clinic, Cleveland, Ohio.
4
Pathology and Laboratory Medicine, Weill Cornell Medicine Physicians, New York, New York.
5
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, New York.
6
Department of Population and Quantitative Health Sciences, CWRU, Cleveland, Ohio.
7
Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
8
Department of Hematology and Oncology, NYU Langone Health, New York, New York.
9
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio. axm788@case.edu.
10
Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio.
#
Contributed equally

Abstract

No predictive biomarkers can robustly identify patients with non-small cell lung cancer (NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes ("delta") in the radiomic texture (DelRADx) of CT patterns both within and outside tumor nodules before and after two to three cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 patients with NSCLC at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted, and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST-derived response. Association of delta-radiomic risk score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an AUC of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, and 0.85 and 0.81 in D2 and D3 DRS was associated with OS [HR: 1.64; 95% confidence interval (CI), 1.22-2.21; P = 0.0011; C-index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in patients with NSCLC.

Supplemental Content

Full text links

Icon for HighWire
Loading ...
Support Center